Accessing a virtual reality environment

ABSTRACT

A method executed by a computing entity includes generating a virtual reality environment utilizing a group of object representations by rendering a representation of a first set of object representations and a second set of object representations to produce first portrayal 3-D video frames of a first piece of information for the virtual reality environment. The method further includes determining whether a second piece of information has been selected based on a second cue. When the second piece of information has been selected the method further includes rendering another representation of the second set of object representations and the first set of object representations to produce second portrayal 3-D video frames of the second piece of information for the virtual reality environment.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Pat. Application claims priority pursuant to 35U.S.C. § 119(e) to U.S. Provisional Application No. 63/290,198, entitled“UPDATING A LESSON PACKAGE FOR A VIRTUAL ENVIRONMENT”, filed Dec. 16,2021, which is hereby incorporated herein by reference in its entiretyand made part of the present U.S. Utility Pat. Application for allpurposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer systems and moreparticularly to computer systems providing educational, training, andentertainment content.

Description of Related Art

Computer systems communicate data, process data, and/or store data. Suchcomputer systems include computing devices that range from wirelesssmart phones, laptops, tablets, personal computers (PC), work stations,personal three-dimensional (3-D) content viewers, and video gamedevices, to data centers where data servers store and provide access todigital content. Some digital content is utilized to facilitateeducation, training, and entertainment. Examples of visual contentincludes electronic books, reference materials, training manuals,classroom coursework, lecture notes, research papers, images, videoclips, sensor data, reports, etc.

A variety of educational systems utilize educational tools andtechniques. For example, an educator delivers educational content tostudents via an education tool of a recorded lecture that has built-infeedback prompts (e.g., questions, verification of viewing, etc.). Theeducator assess a degree of understanding of the educational contentand/or overall competence level of a student from responses to thefeedback prompts.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem in accordance with the present invention;

FIG. 2A is a schematic block diagram of an embodiment of a computingentity of a computing system in accordance with the present invention;

FIG. 2B is a schematic block diagram of an embodiment of a computingdevice of a computing system in accordance with the present invention;

FIG. 3 is a schematic block diagram of another embodiment of a computingdevice of a computing system in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an environmentsensor module of a computing system in accordance with the presentinvention;

FIG. 5A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 5B is a schematic block diagram of an embodiment of arepresentation of a learning experience in accordance with the presentinvention;

FIG. 6 is a schematic block diagram of another embodiment of arepresentation of a learning experience in accordance with the presentinvention;

FIG. 7A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 7B is a schematic block diagram of another embodiment of arepresentation of a learning experience in accordance with the presentinvention;

FIGS. 8A-8C are schematic block diagrams of another embodiment of acomputing system illustrating an example of creating a learningexperience in accordance with the present invention;

FIG. 8D is a logic diagram of an embodiment of a method for creating alearning experience within a computing system in accordance with thepresent invention;

FIGS. 8E, 8F, 8G, 8H, 8J, and 8K are schematic block diagrams of anotherembodiment of a computing system illustrating another example ofcreating a learning experience in accordance with the present invention;

FIG. 9A is a schematic block diagram of a data structure for a smartcontract in accordance with the present invention;

FIGS. 9B and 9C are schematic block diagrams of organization of objectdistributed ledgers in accordance with the present invention;

FIG. 9D is a schematic block diagram of an embodiment of a blockchainassociated with an object distributed ledger in accordance with thepresent invention;

FIGS. 10A, 10B, and 10C are schematic block diagrams of an embodiment ofa computing system illustrating an example of generating a virtualreality environment in accordance with the present invention;

FIGS. 11A, 11B, and 11C are schematic block diagrams of an embodiment ofa computing system illustrating an example of generating multipleresolutions of a virtual reality environment in accordance with thepresent invention;

FIGS. 12A, 12B, and 12C are schematic block diagrams of an embodiment ofa computing system illustrating an example of updating a virtual realityenvironment in accordance with the present invention;

FIGS. 13A and 13B are schematic block diagrams of an embodiment of acomputing system illustrating an example of accessing a virtual realityenvironment in accordance with the present invention; and

FIGS. 14A, 14B, and 14C are schematic block diagrams of an embodiment ofa computing system illustrating another example of updating a lessonpackage in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem 10 that includes a real world environment 12, an environmentsensor module 14, and environment model database 16, a human interfacemodule 18, and a computing entity 20. The real-world environment 12includes places 22, objects 24, instructors 26-1 through 26-N, andlearners 28-1 through 28-N. The computing entity 20 includes anexperience creation module 30, an experience execution module 32, and alearning assets database 34.

The places 22 includes any area. Examples of places 22 includes a room,an outdoor space, a neighborhood, a city, etc. The objects 24 includesthings within the places. Examples of objects 24 includes people,equipment, furniture, personal items, tools, and representations ofinformation (i.e., video recordings, audio recordings, captured text,etc.). The instructors includes any entity (e.g., human or human proxy)imparting knowledge. The learners includes entities trying to gainknowledge and may temporarily serve as an instructor.

In an example of operation of the computing system 10, the experiencecreation module 30 receives environment sensor information 38 from theenvironment sensor module 14 based on environment attributes 36 from thereal world environment 12. The environment sensor information 38includes time-based information (e.g., static snapshot, continuousstreaming) from environment attributes 36 including XYZ positioninformation, place information, and object information (i.e.,background, foreground, instructor, learner, etc.). The XYZ positioninformation includes portrayal in a world space industry standard format(e.g., with reference to an absolute position).

The environment attributes 36 includes detectable measures of thereal-world environment 12 to facilitate generation of amulti-dimensional (e.g., including time) representation of thereal-world environment 12 in a virtual reality and/or augmented realityenvironment. For example, the environment sensor module 14 producesenvironment sensor information 38 associated with a medical examinationroom and a subject human patient (e.g., an MRI). The environment sensormodule 14 is discussed in greater detail with reference to FIG. 4 .

Having received the environment sensor information 38, the experiencecreation module 30 accesses the environment model database 16 to recovermodeled environment information 40. The modeled environment information40 includes a synthetic representation of numerous environments (e.g.,model places and objects). For example, the modeled environmentinformation 40 includes a 3-D representation of a typical humancirculatory system. The models include those that are associated withcertain licensing requirements (e.g., copyrights, etc.).

Having received the modeled environment information 40, the experiencecreation module 30 receives instructor information 44 from the humaninterface module 18, where the human interface module 18 receives humaninput/output (I/O) 42 from instructor 26-1. The instructor information44 includes a representation of an essence of communication with aparticipant instructor. The human I/O 42 includes detectable fundamentalforms of communication with humans or human proxies. The human interfacemodule 18 is discussed in greater detail with reference to FIG. 3 .

Having received the instructor information 44, the experience creationmodule 30 interprets the instructor information 44 to identify aspectsof a learning experience. A learning experience includes numerousaspects of an encounter between one or more learners and an imparting ofknowledge within a representation of a learning environment thatincludes a place, multiple objects, and one or more instructors. Thelearning experience further includes an instruction portion (e.g., actsto impart knowledge) and an assessment portion (e.g., further actsand/or receiving of learner input) to determine a level of comprehensionof the knowledge by the one or more learners. The learning experiencestill further includes scoring of the level of comprehension andtallying multiple learning experiences to facilitate higher-levelcompetency accreditations (e.g., certificates, degrees, licenses,training credits, experiences completed successfully, etc.).

As an example of the interpreting of the instructor information 44, theexperience creation module 30 identifies a set of concepts that theinstructor desires to impart upon a learner and a set of comprehensionverifying questions and associated correct answers. The experiencecreation module 30 further identifies step-by-step instructorannotations associated with the various objects within the environmentof the learning experience for the instruction portion and theassessment portion. For example, the experience creation module 30identifies positions held by the instructor 26-1 as the instructornarrates a set of concepts associated with the subject patientcirculatory system. As a further example, the experience creation module30 identifies circulatory system questions and correct answers posed bythe instructor associated with the narrative.

Having interpreted the instructor information 44, the experiencecreation module 30 renders the environment sensor information 38, themodeled environment information 40, and the instructor information 44 toproduce learning assets information 48 for storage in the learningassets database 34. The learning assets information 48 includes allthings associated with the learning experience to facilitate subsequentrecreation. Examples includes the environment, places, objects,instructors, learners, assets, recorded instruction information,learning evaluation information, etc.

Execution of a learning experience for the one or more learners includesa variety of approaches. A first approach includes the experienceexecution module 32 recovering the learning assets information 48 fromthe learning assets database 34, rendering the learning experience aslearner information 46, and outputting the learner information 46 viathe human interface module 18 as further human I/O 42 to one or more ofthe learners 28-1 through 28-N. The learner information 46 includesinformation to be sent to the one or more learners and informationreceived from the one or more learners. For example, the experienceexecution module 32 outputs learner information 46 associated with theinstruction portion for the learner 28-1 and collects learnerinformation 46 from the learner 28-1 that includes submitted assessmentanswers in response to assessment questions of the assessment portioncommunicated as further learner information 46 for the learner 28-1.

A second approach includes the experience execution module 32 renderingthe learner information 46 as a combination of live streaming ofenvironment sensor information 38 from the real-world environment 12along with an augmented reality overlay based on recovered learningasset information 48. For example, a real world subject human patient ina medical examination room is live streamed as the environment sensorinformation 38 in combination with a prerecorded instruction portionfrom the instructor 26-1.

FIG. 2A is a schematic block diagram of an embodiment of the computingentity 20 of the computing system 10. The computing entity 20 includesone or more computing devices 100-1 through 100-N. A computing device isany electronic device that communicates data, processes data, representsdata (e.g., user interface) and/or stores data.

Computing devices include portable computing devices and fixed computingdevices. Examples of portable computing devices include an embeddedcontroller, a smart sensor, a social networking device, a gaming device,a smart phone, a laptop computer, a tablet computer, a video gamecontroller, and/or any other portable device that includes a computingcore. Examples of fixed computing devices includes a personal computer,a computer server, a cable set-top box, a fixed display device, anappliance, and industrial controller, a video game counsel, a homeentertainment controller, a critical infrastructure controller, and/orany type of home, office or cloud computing equipment that includes acomputing core.

FIG. 2B is a schematic block diagram of an embodiment of a computingdevice 100 of the computing system 10 that includes one or morecomputing cores 52-1 through 52-N, a memory module 102, the humaninterface module 18, the environment sensor module 14, and an I/O module104. In alternative embodiments, the human interface module 18, theenvironment sensor module 14, the I/O module 104, and the memory module102 may be standalone (e.g., external to the computing device). Anembodiment of the computing device 100 will be discussed in greaterdetail with reference to FIG. 3 .

FIG. 3 is a schematic block diagram of another embodiment of thecomputing device 100 of the computing system 10 that includes the humaninterface module 18, the environment sensor module 14, the computingcore 52-1, the memory module 102, and the I/O module 104. The humaninterface module 18 includes one or more visual output devices 74 (e.g.,video graphics display, 3-D viewer, touchscreen, LED, etc.), one or morevisual input devices 80 (e.g., a still image camera, a video camera, a3-D video camera, photocell, etc.), and one or more audio output devices78 (e.g., speaker(s), headphone jack, a motor, etc.). The humaninterface module 18 further includes one or more user input devices 76(e.g., keypad, keyboard, touchscreen, voice to text, a push button, amicrophone, a card reader, a door position switch, a biometric inputdevice, etc.) and one or more motion output devices 106 (e.g., servos,motors, lifts, pumps, actuators, anything to get real-world objects tomove).

The computing core 52-1 includes a video graphics module 54, one or moreprocessing modules 50-1 through 50-N, a memory controller 56, one ormore main memories 58-1 through 58-N (e.g., RAM), one or moreinput/output (I/O) device interface modules 62, an input/output (I/O)controller 60, and a peripheral interface 64. A processing module is asdefined at the end of the detailed description.

The memory module 102 includes a memory interface module 70 and one ormore memory devices, including flash memory devices 92, hard drive (HD)memory 94, solid state (SS) memory 96, and cloud memory 98. The cloudmemory 98 includes an on-line storage system and an on-line backupsystem.

The I/O module 104 includes a network interface module 72, a peripheraldevice interface module 68, and a universal serial bus (USB) interfacemodule 66. Each of the I/O device interface module 62, the peripheralinterface 64, the memory interface module 70, the network interfacemodule 72, the peripheral device interface module 68, and the USBinterface modules 66 includes a combination of hardware (e.g.,connectors, wiring, etc.) and operational instructions stored on memory(e.g., driver software) that are executed by one or more of theprocessing modules 50-1 through 50-N and/or a processing circuit withinthe particular module.

The I/O module 104 further includes one or more wireless location modems84 (e.g., global positioning satellite (GPS), Wi-Fi, angle of arrival,time difference of arrival, signal strength, dedicated wirelesslocation, etc.) and one or more wireless communication modems 86 (e.g.,a cellular network transceiver, a wireless data network transceiver, aWi-Fi transceiver, a Bluetooth transceiver, a 315 MHz transceiver, a zigbee transceiver, a 60 GHz transceiver, etc.). The I/O module 104 furtherincludes a telco interface 108 (e.g., to interface to a public switchedtelephone network), a wired local area network (LAN) 88 (e.g., optical,electrical), and a wired wide area network (WAN) 90 (e.g., optical,electrical). The I/O module 104 further includes one or more peripheraldevices (e.g., peripheral devices 1-P) and one or more universal serialbus (USB) devices (USB devices 1-U). In other embodiments, the computingdevice 100 may include more or less devices and modules than shown inthis example embodiment.

FIG. 4 is a schematic block diagram of an embodiment of the environmentsensor module 14 of the computing system 10 that includes a sensorinterface module 120 to output environment sensor information 150 basedon information communicated with a set of sensors. The set of sensorsincludes a visual sensor 122 (e.g., to the camera, 3-D camera, 360° viewcamera, a camera array, an optical spectrometer, etc.) and an audiosensor 124 (e.g., a microphone, a microphone array). The set of sensorsfurther includes a motion sensor 126 (e.g., a solid-state Gyro, avibration detector, a laser motion detector) and a position sensor 128(e.g., a Hall effect sensor, an image detector, a GPS receiver, a radarsystem).

The set of sensors further includes a scanning sensor 130 (e.g., CATscan, MRI, x-ray, ultrasound, radio scatter, particle detector, lasermeasure, further radar) and a temperature sensor 132 (e.g., thermometer,thermal coupler). The set of sensors further includes a humidity sensor134 (resistance based, capacitance based) and an altitude sensor 136(e.g., pressure based, GPS-based, laser-based).

The set of sensors further includes a biosensor 138 (e.g., enzyme,immuno, microbial) and a chemical sensor 140 (e.g., mass spectrometer,gas, polymer). The set of sensors further includes a magnetic sensor 142(e.g., Hall effect, piezo electric, coil, magnetic tunnel junction) andany generic sensor 144 (e.g., including a hybrid combination of two ormore of the other sensors).

FIG. 5A is a schematic block diagram of another embodiment of acomputing system that includes the environment model database 16, thehuman interface module 18, the instructor 26-1, the experience creationmodule 30, and the learning assets database 34 of FIG. 1 . In an exampleof operation, the experience creation module 30 obtains modeledenvironment information 40 from the environment model database 16 andrenders a representation of an environment and objects of the modeledenvironment information 40 to output as instructor output information160. The human interface module 18 transforms the instructor outputinformation 160 into human output 162 for presentation to the instructor26-1. For example, the human output 162 includes a 3-D visualization andstereo audio output.

In response to the human output 162, the human interface module 18receives human input 164 from the instructor 26-1. For example, thehuman input 164 includes pointer movement information and human speechassociated with a lesson. The human interface module 18 transforms thehuman input 164 into instructor input information 166. The instructorinput information 166 includes one or more of representations ofinstructor interactions with objects within the environment and explicitevaluation information (e.g., questions to test for comprehension level,and correct answers to the questions).

Having received the instructor input information 166, the experiencecreation module 30 renders a representation of the instructor inputinformation 166 within the environment utilizing the objects of themodeled environment information 40 to produce learning asset information48 for storage in the learnings assets database 34. Subsequent access ofthe learning assets information 48 facilitates a learning experience.

FIG. 5B is a schematic block diagram of an embodiment of arepresentation of a learning experience that includes a virtual place168 and a resulting learning objective 170. A learning objectiverepresents a portion of an overall learning experience, where thelearning objective is associated with at least one major concept ofknowledge to be imparted to a learner. The major concept may includeseveral sub-concepts. The makeup of the learning objective is discussedin greater detail with reference to FIG. 6 .

The virtual place 168 includes a representation of an environment (e.g.,a place) over a series of time intervals (e.g., time 0-N). Theenvironment includes a plurality of objects 24-1 through 24-N. At eachtime reference, the positions of the objects can change in accordancewith the learning experience. For example, the instructor 26-1 of FIG.5A interacts with the objects to convey a concept. The sum of thepositions of the environment and objects within the virtual place 168 iswrapped into the learning objective 170 for storage and subsequentutilization when executing the learning experience.

FIG. 6 is a schematic block diagram of another embodiment of arepresentation of a learning experience that includes a plurality ofmodules 1-N. Each module includes a set of lessons 1-N. Each lessonincludes a plurality of learning objectives 1-N. The learning experiencetypically is played from left to right where learning objectives aresequentially executed in lesson 1 of module 1 followed by learningobjectives of lesson 2 of module 1 etc.

As learners access the learning experience during execution, theordering may be accessed in different ways to suit the needs of theunique learner based on one or more of preferences, experience,previously demonstrated comprehension levels, etc. For example, aparticular learner may skip over lesson 1 of module 1 and go right tolesson 2 of module 1 when having previously demonstrated competency ofthe concepts associated with lesson 1.

Each learning objective includes indexing information, environmentinformation, asset information, instructor interaction information, andassessment information. The index information includes one or more ofcategorization information, topics list, instructor identification,author identification, identification of copyrighted materials,keywords, concept titles, prerequisites for access, and links to relatedlearning objectives.

The environment information includes one or more of structureinformation, environment model information, background information,identifiers of places, and categories of environments. The assetinformation includes one or more of object identifiers, objectinformation (e.g., modeling information), asset ownership information,asset type descriptors (e.g., 2-D, 3-D). Examples include models ofphysical objects, stored media such as videos, scans, images, digitalrepresentations of text, digital audio, and graphics.

The instructor interaction information includes representations ofinstructor annotations, actions, motions, gestures, expressions, eyemovement information, facial expression information, speech, and speechinflections. The content associated with the instructor interactioninformation includes overview information, speaker notes, actionsassociated with assessment information, (e.g., pointing to questions,revealing answers to the questions, motioning related to posingquestions) and conditional learning objective execution orderinginformation (e.g., if the learner does this then take this path,otherwise take another path).

The assessment information includes a summary of desired knowledge toimpart, specific questions for a learner, correct answers to thespecific questions, multiple-choice question sets, and scoringinformation associated with writing answers. The assessment informationfurther includes historical interactions by other learners with thelearning objective (e.g., where did previous learners look most oftenwithin the environment of the learning objective, etc.), historicalresponses to previous comprehension evaluations, and actions tofacilitate when a learner responds with a correct or incorrect answer(e.g., motion stimulus to activate upon an incorrect answer to increasea human stress level).

FIG. 7A is a schematic block diagram of another embodiment of acomputing system that includes the learning assets database 34, theexperience execution module 32, the human interface module 18, and thelearner 28-1 of FIG. 1 . In an example of operation, the experienceexecution module 32 recovers learning asset information 48 from thelearning assets database 34 (e.g., in accordance with a selection by thelearner 28-1). The experience execution module 32 renders a group oflearning objectives associated with a common lesson within anenvironment utilizing objects associated with the lesson to producelearner output information 172. The learner output information 172includes a representation of a virtual place and objects that includesinstructor interactions and learner interactions from a perspective ofthe learner.

The human interface module 18 transforms the learner output information172 into human output 162 for conveyance of the learner outputinformation 172 to the learner 28-1. For example, the human interfacemodule 18 facilitates displaying a 3-D image of the virtual environmentto the learner 28-1.

The human interface module 18 transforms human input 164 from thelearner 28-1 to produce learner input information 174. The learner inputinformation 174 includes representations of learner interactions withobjects within the virtual place (e.g., answering comprehension levelevaluation questions).

The experience execution module 32 updates the representation of thevirtual place by modifying the learner output information 172 based onthe learner input information 174 so that the learner 28-1 enjoysrepresentations of interactions caused by the learner within the virtualenvironment. The experience execution module 32 evaluates the learnerinput information 174 with regards to evaluation information of thelearning objectives to evaluate a comprehension level by the learner28-1 with regards to the set of learning objectives of the lesson.

FIG. 7B is a schematic block diagram of another embodiment of arepresentation of a learning experience that includes the learningobjective 170 and the virtual place 168. In an example of operation, thelearning objective 170 is recovered from the learning assets database 34of FIG. 7A and rendered to create the virtual place 168 representationsof objects 24-1 through 24-N in the environment from time referenceszero through N. For example, a first object is the instructor 26-1 ofFIG. 5A, a second object is the learner 28-1 of FIG. 7A, and theremaining objects are associated with the learning objectives of thelesson, where the objects are manipulated in accordance with annotationsof instructions provided by the instructor 26-1.

The learner 28-1 experiences a unique viewpoint of the environment andgains knowledge from accessing (e.g., playing) the learning experience.The learner 28-1 further manipulates objects within the environment tosupport learning and assessment of comprehension of objectives of thelearning experience.

FIGS. 8A-8C are schematic block diagrams of another embodiment of acomputing system illustrating an example of creating a learningexperience. The computing system includes the environment model database16, the experience creation module 30, and the learning assets database34 of FIG. 1 . The experience creation module 30 includes a learningpath module 180, an asset module 182, an instruction module 184, and alesson generation module 186.

In an example of operation, FIG. 8 A illustrates the learning pathmodule 180 determining a learning path (e.g., structure and ordering oflearning objectives to complete towards a goal such as a certificate ordegree) to include multiple modules and/or lessons. For example, thelearning path module 180 obtains learning path information 194 from thelearning assets database 34 and receives learning path structureinformation 190 and learning objective information 192 (e.g., from aninstructor) to generate updated learning path information 196.

The learning path structure information 190 includes attributes of thelearning path and the learning objective information 192 includes asummary of desired knowledge to impart. The updated learning pathinformation 196 is generated to include modifications to the learningpath information 194 in accordance with the learning path structureinformation 190 in the learning objective information 192.

The asset module 182 determines a collection of common assets for eachlesson of the learning path. For example, the asset module 182 receivessupporting asset information 198 (e.g., representation information ofobjects in the virtual space) and modeled asset information 200 from theenvironment model database 16 to produce lesson asset information 202.The modeled asset information 200 includes representations of anenvironment to support the updated learning path information 196 (e.g.,modeled places and modeled objects) and the lesson asset information 202includes a representation of the environment, learning path, theobjectives, and the desired knowledge to impart.

FIG. 8B further illustrates the example of operation where theinstruction module 184 outputs a representation of the lesson assetinformation 202 as instructor output information 160. The instructoroutput information 160 includes a representation of the environment andthe asset so far to be experienced by an instructor who is about toinput interactions with the environment to impart the desired knowledge.

The instruction module 184 receives instructor input information 166from the instructor in response to the instructor output information160. The instructor input information 166 includes interactions from theinstructor to facilitate imparting of the knowledge (e.g., instructorannotations, pointer movements, highlighting, text notes, and speech)and testing of comprehension of the knowledge (e.g., valuationinformation such as questions and correct answers). The instructionmodule 184 obtains assessment information (e.g., comprehension testpoints, questions, correct answers to the questions) for each learningobjective based on the lesson asset information 202 and producesinstruction information 204 (e.g., representation of instructorinteractions with objects within the virtual place, evaluationinformation).

FIG. 8C further illustrates the example of operation where the lessongeneration module 186 renders (e.g., as a multidimensionalrepresentation) the objects associated with each lesson (e.g., assets ofthe environment) within the environment in accordance with theinstructor interactions for the instruction portion and the assessmentportion of the learning experience. Each object is assigned a relativeposition in XYZ world space within the environment to produce the lessonrendering.

The lesson generation module 186 outputs the rendering as a lessonpackage 206 for storage in the learning assets database 34. The lessonpackage 206 includes everything required to replay the lesson for asubsequent learner (e.g., representation of the environment, theobjects, the interactions of the instructor during both the instructionand evaluation portions, questions to test comprehension, correctanswers to the questions, a scoring approach for evaluatingcomprehension, all of the learning objective information associated witheach learning objective of the lesson).

FIG. 8D is a logic diagram of an embodiment of a method for creating alearning experience within a computing system (e.g., the computingsystem 10 of FIG. 1 ). In particular, a method is presented inconjunction with one or more functions and features described inconjunction with FIGS. 1-7B, and also FIGS. 8A-8C. The method includesstep 220 where a processing module of one or more processing modules ofone or more computing devices within the computing system determinesupdated learning path information based on learning path information,learning path structure information, and learning objective information.For example, the processing module combines a previous learning pathwith obtained learning path structure information in accordance withlearning objective information to produce the updated learning pathinformation (i.e., specifics for a series of learning objectives of alesson).

The method continues at step 222 where the processing module determineslesson asset information based on the updated learning path information,supporting asset information, and modeled asset information. Forexample, the processing module combines assets of the supporting assetinformation (e.g., received from an instructor) with assets and a placeof the modeled asset information in accordance with the updated learningpath information to produce the lesson asset information. The processingmodule selects assets as appropriate for each learning objective (e.g.,to facilitate the imparting of knowledge based on a predeterminationand/or historical results).

The method continues at step 224 where the processing module obtainsinstructor input information. For example, the processing module outputsa representation of the lesson asset information as instructor outputinformation and captures instructor input information for each lesson inresponse to the instructor output information. Further obtain assetinformation for each learning objective (e.g., extract from theinstructor input information).

The method continues at step 226 where the processing module generatesinstruction information based on the instructor input information. Forexample, the processing module combines instructor gestures and furtherenvironment manipulations based on the assessment information to producethe instruction information.

The method continues at step 228 where the processing module renders,for each lesson, a multidimensional representation of environment andobjects of the lesson asset information utilizing the instructioninformation to produce a lesson package. For example, the processingmodule generates the multidimensional representation of the environmentthat includes the objects and the instructor interactions of theinstruction information to produce the lesson package. For instance, theprocessing module includes a 3-D rendering of a place, backgroundobjects, recorded objects, and the instructor in a relative position XYZworld space over time.

The method continues at step 230 where the processing module facilitatesstorage of the lesson package. For example, the processing moduleindexes the one or more lesson packages of the one or more lessons ofthe learning path to produce indexing information (e.g., title, author,instructor identifier, topic area, etc.). The processing module storesthe indexed lesson package as learning asset information in a learningassets database.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 8E, 8F, 8G, 8H, 8J, and 8K are schematic block diagrams of anotherembodiment of a computing system illustrating another example of amethod to create a learning experience. The embodiment includes creatinga multi-disciplined learning tool regarding a topic. Themulti-disciplined aspect of the learning tool includes both disciplinesof learning and any form/format of presentation of content regarding thetopic. For example, a first discipline includes mechanical systems, asecond discipline includes electrical systems, and a third disciplineincludes fluid systems when the topic includes operation of a combustionbased engine. The computing system includes the environment modeldatabase 16 of FIG. 1 , the learning assets database 34 of FIG. 1 , andthe experience creation module 30 of FIG. 1 .

FIG. 8E illustrates the example of operation where the experiencecreation module 30 creates a first-pass of a first learning object 700-1for a first piece of information regarding the topic to include a firstset of knowledge bullet-points 702-1 regarding the first piece ofinformation. The creating includes utilizing guidance from an instructorand/or reusing previous knowledge bullet-points for a related topic. Forexample, the experience creation module 30 extracts the bullet-pointsfrom one or more of learning path structure information 190 and learningobjective information 192 when utilizing the guidance from theinstructor. As another example, the experience creation module 30extracts the bullet-points from learning path information 194 retrievedfrom the learning assets database 34 when utilizing previous knowledgebullet-points for the related topic.

Each piece of information is to impart additional knowledge related tothe topic. The additional knowledge of the piece of information includesa characterization of learnable material by most learners in just a fewminutes. As a specific example, the first piece of information includes“4 cycle engine intake cycles” when the topic includes “how a 4 cycleengine works.”

Each of the knowledge bullet-points are to impart knowledge associatedwith the associated piece of information in a logical (e.g., sequential)and knowledge building fashion. As a specific example, the experiencecreation module 30 creates the first set of knowledge bullet-points702-1 based on instructor input to include a first bullet-point “intakestroke: intake valve opens, air/fuel mixture pulled into cylinder bypiston” and a second bullet-point “compression stroke: intake valvecloses, piston compresses air/fuel mixture in cylinder” when the firstpiece of information includes the “4 cycle engine intake cycles.”

FIG. 8F further illustrates the example of operation where theexperience creation module 30 creates a first-pass of a second learningobject 700-2 for a second piece of information regarding the topic toinclude a second set of knowledge bullet-points 702-2 regarding thesecond piece of information. As a specific example, the experiencecreation module 30 creates the second set of knowledge bullet-points702-2 based on the instructor input to include a first bullet-point“power stroke: spark plug ignites air/fuel mixture pushing piston” and asecond bullet-point “exhaust stroke: exhaust valve opens and pistonpushes exhaust out of cylinder, exhaust valve closes” when the secondpiece of information includes “4 cycle engine outtake cycles.”

FIG. 8G further illustrates the example of operation where theexperience creation module 30 obtains illustrative assets 704 based onthe first and second set of knowledge bullet-points 702-1 and 702-2. Theillustrative assets 704 depicts one or more aspects regarding the topicpertaining to the first and second pieces of information. Examples ofillustrative assets includes background environments, objects within theenvironment (e.g., things, tools), where the objects and the environmentare represented by multidimensional models (e.g., 3-D model) utilizing avariety of representation formats including video, scans, images, text,audio, graphics etc.

The obtaining of the illustrative assets 704 includes a variety ofapproaches. A first approach includes interpreting instructor inputinformation to identify the illustrative asset. For example, theexperience creation module 30 interprets instructor input information toidentify a cylinder asset.

A second approach includes identifying a first object of the first andsecond set of knowledge bullet-points as an illustrative asset. Forexample, the experience creation module 30 identifies the piston objectfrom both the first and second set of knowledge bullet-points.

A third approach includes determining the illustrative assets 704 basedon the first object of the first and second set of knowledgebullet-points. For example, the experience creation module 30 accessesthe environment model database 16 to extract information about an assetfrom one or more of supporting asset information 198 and modeled assetinformation 200 for a sparkplug when interpreting the first and secondset of knowledge bullet-points.

FIG. 8H further illustrates the example of operation where theexperience creation module 30 creates a second-pass of the firstlearning object 700-1 to further include first descriptive assets 706-1regarding the first piece of information based on the first set ofknowledge bullet-points 702-1 and the illustrative assets 704.Descriptive assets include instruction information that utilizes theillustrative asset 704 to impart knowledge and subsequently test forknowledge retention. The embodiments of the descriptive assets includesmultiple disciplines and multiple dimensions to provide improvedlearning by utilizing multiple senses of a learner. Examples of theinstruction information includes annotations, actions, motions,gestures, expressions, recorded speech, speech inflection information,review information, speaker notes, and assessment information.

The creating the second-pass of the first learning object 700-1 includesgenerating a representation of the illustrative assets 704 based on afirst knowledge bullet-point of the first set of knowledge bullet-points702-1. For example, the experience creation module 30 renders 3-D framesof a 3-D model of the cylinder, the piston, the spark plug, the intakevalve, and the exhaust valve in motion when performing the intake strokewhere the intake valve opens and the air/fuel mixture is pulled into thecylinder by the piston.

The creating of the second-pass of the first learning object 700-1further includes generating the first descriptive assets 706-1 utilizingthe representation of the illustrative assets 704. For example, theexperience creation module 30 renders 3-D frames of the 3-D models ofthe various engine parts without necessarily illustrating the first setof knowledge bullet-points 702-1.

In an embodiment where the experience creation module 30 generates therepresentation of the illustrative assets 704, the experience creationmodule 30 outputs the representation of the illustrative asset 704 asinstructor output information 160 to an instructor. For example, the 3-Dmodel of the cylinder and associated parts.

The experience creation module 30 receives instructor input information166 in response to the instructor output information 160. For example,the instructor input information 166 includes instructor annotations tohelp explain the intake stroke (e.g., instructor speech, instructorpointer motions). The experience creation module 30 interprets theinstructor input information 166 to produce the first descriptive assets706-1. For example, the renderings of the engine parts include theintake stroke as annotated by the instructor.

FIG. 8J further illustrates the example of operation where theexperience creation module 30 creates a second-pass of the secondlearning object 700-2 to further include second descriptive assets 706-2regarding the second piece of information based on the second set ofknowledge bullet-points 702-2 and the illustrative assets 704. Forexample, the experience creation module 30 creates 3-D renderings of thepower stroke and the exhaust stroke as annotated by the instructor basedon further instructor input information 166.

FIG. 8K further illustrates the example of operation where theexperience creation module 30 links the second-passes of the first andsecond learning objects 700-1 and 700-2 together to form at least aportion of the multi-disciplined learning tool. For example, theexperience creation module 30 aggregates the first learning object 700-1and the second learning object 700-2 to produce a lesson package 206 forstorage in the learning assets database 34.

In an embodiment, the linking of the second-passes of the first andsecond learning objects 700-1 and 700-2 together to form the at leastthe portion of the multi-disciplined learning tool includes generatingindex information for the second-passes of first and second learningobjects to indicate sharing of the illustrative asset 704. For example,the experience creation module 30 generates the index information toidentify the first learning object 700-1 and the second learning object700-2 as related to the same topic.

The linking further includes facilitating storage of the indexinformation and the first and second learning objects 700-1 and 700-2 inthe learning assets database 34 to enable subsequent utilization of themulti-disciplined learning tool. For example, the experience creationmodule 30 aggregates the first learning object 700-1, the secondlearning object 700-2, and the index information to produce the lessonpackage 206 for storage in the learning assets database 34.

The method described above with reference to FIGS. 8E-8K in conjunctionwith the experience creation module 30 can alternatively be performed byother modules of the computing system 10 of FIG. 1 or by other devicesincluding various embodiments of the computing entity 20 of FIG. 2A. Inaddition, at least one memory section (e.g., a computer readable memory,a non-transitory computer readable storage medium, a non-transitorycomputer readable memory organized into a first memory element, a secondmemory element, a third memory element, a fourth element section, afifth memory element, a sixth memory element, etc.) that storesoperational instructions can, when executed by one or more processingmodules of the one or more computing entities of the computing system10, cause the one or more computing devices to perform any or all of themethod steps described above.

FIG. 9A is a schematic block diagram of a data structure for a smartcontract 300 that includes object information 302 and license terms 304.The object information 302 includes object basics (e.g., including linksto blockchains and electronic assets), available license terms, andavailable patent terms. FIG. 9A illustrates examples of each category ofthe object information 302. Examples of an object of the objectinformation 302 that are associated with training and educationofferings include a university course, an education curriculum, aneducation degree, a training program, a training session, a lesson, alesson package, and a learning object. Examples of the object of theobject information 302 that are associated with a student include aperson, a group of students, a class, people that work for a commonemployer, etc.

The license terms 304 includes licensee information, agreed licenseterms, and agreed payment terms. FIG. 9A further illustrates examples ofeach of the categories of the license terms 304.

FIGS. 9B and 9C are schematic block diagrams of organization of objectdistributed ledgers. FIG. 9B illustrates an example where a singleblockchain serves as the object distributed ledger linking a series ofblocks of the blockchain, where each block is associated with adifferent license (e.g., use of training) for a training objectassociated with a non-fungible token. FIG. 9C illustrates anotherexample where a first blockchain links a series of blocks of differentnon-fungible tokens for different sets of training object licenses. Eachblock forms a blockchain of its own where each further block of its ownis associated with a different license for the set of training objectsof the non-fungible token.

FIG. 9D is a schematic block diagram of an embodiment of contentblockchain of an object distributed ledger, where the content includesthe smart contract as previously discussed. The content blockchainincludes a plurality of blocks 2-4. Each block includes a header sectionand a transaction section. The header section includes one or more of anonce, a hash of a preceding block of the blockchain, where thepreceding block was under control of a preceding device (e.g., a brokercomputing device, a user computing device, a blockchain node computingdevice, etc.) in a chain of control of the blockchain, and a hash of acurrent block (e.g., a current transaction section), where the currentblock is under control of a current device in the chain of control ofthe blockchain.

The transaction section includes one or more of a public key of thecurrent device, a signature of the preceding device, smart contractcontent, change of control from the preceding device to the currentdevice, and content information from the previous block as received bythe previous device plus content added by the previous device whentransferring the current block to the current device.

FIG. 9D further includes devices 2-3 to facilitate illustration ofgeneration of the blockchain. Each device includes a hash function, asignature function, and storage for a public/private key pair generatedby the device.

An example of operation of the generating of the blockchain, when thedevice 2 has control of the blockchain and is passing control of theblockchain to the device 3 (e.g., the device 3 is transacting a transferof content from device 2), the device 2 obtains the device 3 public keyfrom device 3, performs a hash function 2 over the device 3 public keyand the transaction 2 to produce a hashing resultant (e.g., precedingtransaction to device 2) and performs a signature function 2 over thehashing resultant utilizing a device 2 private key to produce a device 2signature.

Having produced the device 2 signature, the device 2 generates thetransaction 3 to include the device 3 public key, the device 2signature, device 3 content request to 2 information, and the previouscontent plus content from device 2. The device 3 content request todevice 2 information includes one or more of a detailed content request,a query request, background content, and specific instructions fromdevice 3 to device 2 for access to a patent license. The previouscontent plus content from device 2 includes one or more of content froman original source, content from any subsequent source after theoriginal source, an identifier of a source of content, a serial numberof the content, an expiration date of the content, content utilizationrules, and results of previous blockchain validations.

Having produced the transaction 3 section of the block 3 a processingmodule (e.g., of the device 2, of the device 3, of a transaction miningserver, of another server), generates the header section by performing ahashing function over the transaction section 3 to produce a transaction3 hash, performing the hashing function over the preceding block (e.g.,block 2) to produce a block 2 hash. The performing of the hashingfunction may include generating a nonce such that when performing thehashing function to include the nonce of the header section, a desiredcharacteristic of the resulting hash is achieved (e.g., a desired numberof preceding zeros is produced in the resulting hash).

Having produced the block 3, the device 2 sends the block 3 to thedevice 3, where the device 3 initiates control of the blockchain. Havingreceived the block 3, the device 3 validates the received block 3. Thevalidating includes one or more of verifying the device 2 signature overthe preceding transaction section (e.g., transaction 2) and the device 3public key utilizing the device 2 public key (e.g., a re-createdsignature function result compares favorably to device 2 signature) andverifying that an extracted device 3 public key of the transaction 3compares favorably to the device 3 public key held by the device 3. Thedevice 3 considers the received block 3 validated when the verificationsare favorable (e.g., the authenticity of the associated content istrusted).

FIGS. 10A, 10B, and 10C are schematic block diagrams of an embodiment ofa computing system illustrating an example of generating a virtualreality environment. The computing system includes the human interfacemodule 18 of FIG. 1 , the experience creation module 30 of FIG. 1 , thelearning assets database 34 FIG. 1 , and the environment model database16 of FIG. 1 .

FIG. 10A illustrates an example method of operation of the generatingthe virtual reality environment utilizing a group of objectrepresentations in accordance with interaction information for at leastsome of the object representations of the group of objectrepresentations. At least some of the object representations areassociated with corresponding three dimensional (3-D) physical objects.The interaction information includes 3-D models and position informationfor the at least some of the object representations of the group ofobject representations. A first set of object representations of thegroup of object representations is associated with a first piece ofinformation regarding the topic. A second set of object representationsof the group of object representations is associated with a second pieceof information regarding the topic,

A first step of the example method of operation includes the experiencecreation module 30 obtaining the first and second pieces of informationfor the topic. The obtaining includes creating first and second sets ofknowledge bullet-points of a plurality of learning objects for thetopic. A first learning object of the plurality of learning objectsincludes a first set of knowledge bullet-points for a first piece ofinformation regarding the topic. A second learning object of theplurality of learning objects includes a second set of knowledgebullet-points for a second piece of information regarding the topic. Forexample, the experience creation module 30 receives instructor inputinformation 166, through an appropriate user interface, from the humaninterface module 18 in response to human input 164 from an instructorwhen the topic is how a four stroke internal combustion engine operates.The experience creation module 30 interprets instructor inputinformation 166 to generate the first set of knowledge bullet-points702-1 to include “intake stroke: intake valve opens, air/fuel mixturepulled into cylinder by piston; compression stroke: intake valve closes,piston compresses air/fuel mixture in cylinder.”

The experience creation module 30 interprets further instructor inputinformation 166 to generate the second set of knowledge bullet-points702-2. For example, the experience creation module 30 generates thesecond set of knowledge bullet-points to include “power stroke:sparkplug ignites air/fuel mixture pushing piston; exhaust stroke:exhaust valve opens and piston pushes exhaust out of cylinder, exhaustvalve closes”.

A second step of the example method of operation includes the experiencecreation module 30 identifying a set of common illustrative assets asillustrative assets 704 based on the first and second set of objectrepresentations. The set of common illustrative assets belongs to thefirst and second sets of object representations and depict one or moreaspects regarding the topic pertaining to the first and second pieces ofinformation.

The identifying the set of common illustrative assets includes a varietyof approaches. A first approach includes interpreting instructor inputinformation to identify the common illustrative assets. For example, theexperience creation module 30 interprets instructor input information166 to extract the common illustrative assets.

A second approach includes identifying a common object representation ofthe first and second sets of object representations as the set of commonillustrative assets. For example, the experience creation module 30determines that the piston asset is common to both the first and secondsets of object representations. As another example, the experiencecreation module 30 interprets the first and second set of knowledgebullet-points to identify common objects to produce the illustrativeasset 704. For instance, the experience creation module 30 generates theillustrative asset 704 to include cylinder, piston, sparkplug, intakevalve, exhaust valve.

A third step of the example method of operation includes the experiencecreation module 30 obtaining object representations for the topic bydetermining a preliminary set of lesson assets 705 based on the firstand second learning objects so far and modeled asset information 200.The preliminary set of lesson assets includes a first descriptive assetassociated with the first set of knowledge bullet-points and a seconddescriptive asset associated with the second set of knowledgebullet-points. The first learning object further includes a firstdescriptive asset regarding the first piece of information based on thefirst set of knowledge bullet-points and illustrative assets 704. Thesecond learning object further includes a second descriptive assetregarding the second piece of information based on the second set ofknowledge bullet-points and the common illustrative assets.

For example, the experience creation module 30 determines thepreliminary set of lesson asset 705 to include instructions for each ofthe four strokes (e.g., part of the bullet-points). The preliminary setof lesson asset 705 further includes engine depiction assets for each ofthe four strokes that utilize the common illustrative assets 704 andutilize models of the internal combustion engine from the modeled assetinformation 200.

Alternatively, or in addition to, the experience creation module 30produces the object representations via a series of sub-steps. A firstsub-step includes the experience creation module outputting arepresentation of a set of common illustrative assets as instructoroutput information. For example, descriptions of the cylinder, thepiston, the spark plug, the intake valve, and the exhaust valve.

A second sub-step includes receiving instructor input information 166 inresponse to the instructor output information. For example, theinstructor input information 166 includes guidance with regards to howthe common illustrative assets operate together to produce the fourstrokes of the engine.

A third sub-step includes interpreting the instructor input information166 to produce at least some of the group of object representations asthe preliminary set of lesson assets 705. For example, the experiencecreation module 30 generates the preliminary set of lesson assets 705 toinclude instruction information for each of the four strokes utilizingthe common illustrative assets.

Further alternatively, or further in addition to, the experiencecreation module 30 produces the group of object representations via aseries of operations. A first operation includes the experience creationmodule 30 interpreting the first set of knowledge bullet points of thetopic to produce the first piece of information regarding the topic. Forexample, the experience creation module 30 interprets the intake andcompression strokes bullet points to produce the first piece ofinformation with regards to preparing the cylinder for firing.

A second operation includes the experience creation module 30 obtainingthe first set of object representations based on the first piece ofinformation regarding the topic. For example, the experience creationmodule 30 identifies the first set of object representations frommodeled asset information 200 from the environment model database 16based on the first piece of information for preparing the cylinder forfiring.

A third operation includes the experience creation module 30interpreting the second set of knowledge bullet points of the topic toproduce the second piece of information regarding the topic. Forexample, the experience creation module 30 interprets the power andexhaust strokes bullet points to produce the second piece of informationwith regards to firing the cylinder.

A fourth operation includes the experience creation module 30 obtainingthe second set of object representations based on the second piece ofinformation regarding the topic. For example, the experience creationmodule 30 identifies the second set of object representations from themodeled asset information 200 based on the second piece of informationfor firing the cylinder.

FIG. 10B further illustrates the example method of operation forgenerating the virtual reality environment where a fourth step includesthe experience creation module 30 determining a priority asset 707 ofthe set of common illustrative assets. The priority asset is associatedwith an importance status level that is greater than an importancestatus threshold level with regards to the topic. The priority assetsare associated with a focus of the lesson package of the virtual realityenvironment and are considered to be of higher importance than otherassets. For example, the experience creation module 30 identifies thepiston object as the priority asset when the piston is included in eachof the bullet-points.

The determining the priority asset of the set of common illustrativeassets includes a series of sub-steps. A first sub-step includesdetermining a first importance status level of a first commonillustrative asset of the set of common illustrative assets for example,the experience creation module 30 interprets modeled asset information200 with regards to the piston object to reveal the first importancestatus level of the piston object.

A second sub-step includes comparing the first importance status levelto the importance status threshold level with regards to the topic. Forexample, the experience creation module 30 interprets the modeled assetinformation 200 with regards to the topic to reveal the importancestatus threshold level with regards to the engine operation topic. Theexperience creation module 30 compares the importance status level ofthe piston to the importance status level threshold with regards to theengine topic.

A third sub-step includes establishing the first common illustrativeasset as the priority asset when the first importance status level isgreater than the importance status threshold level with regards to thetopic. For example, the experience creation module 30 establishes thepiston asset as the priority asset when the importance status level ofthe piston is greater than the importance status threshold level.

Having established the priority asset, a fifth step of the examplemethod of operation of generating the virtual reality environmentincludes the experience creation module 30 rendering the priority assetutilizing a first level of resolution to produce a set of priority assetvideo frames. The first level resolution includes a higher than othersresolution level to produce an improved representation of the prioritypiston object to promote improved information retention. For example,the experience creation module 30 renders the priority piston objectwith a higher resolution level than others to produce the set ofpriority asset video frames with regards to the piston.

The fifth step of the example method of operation further includes theexperience creation module 30 selecting a subset of the set of priorityasset video frames to produce a common portion of video frames for thevirtual reality environment with regards to the first and second sets ofobject representations to reduce duplicative rendering. For example, theexperience creation module 30 selects certain frames of the priorityasset video frames that are expected to be utilized to represent boththe first and second pieces of information such that re-rendering ofthose frames is unnecessary to abate unnecessary utilization ofprocessing power of the experience creation module 30.

The selecting the subset of the set of priority asset video frames toproduce the common portion of video frames for the virtual realityenvironment with regards to the first and second sets of objectrepresentations includes a series of sub-steps. A first sub-stepincludes the experience creation module 30 identifying a first priorityasset video frame of the set of priority asset video frames thatrepresents a first aspect of the first set of object representations.For example, the experience creation module 30 identifies a frame of thepiston within the cylinder of the first set of object representations.

A second sub-step includes the experience creation module 30 identifyinga second priority asset video frame of the set of priority asset videoframes that represents a second aspect of the second set of objectrepresentations. For example, the experience creation module 30identifies another piston frame once again within the cylinder of thesecond set of object representations

A third sub-step includes the experience creation module 30 establishingthe common portion of video frames to include the first priority assetvideo frame when more than a minimum threshold number of pixels of thefirst and second priority asset video frames are the same. For example,the experience creation module compares pixels of the frame of thepiston with pixels of the other piston frame and establishes the commonportion of the video frames to include the frame of the piston when morethan the minimum threshold number of pixels of the comparison of thesame.

The fifth step of the example method of operation further includes theexperience creation module 30 rendering another representation of thefirst set of object representations utilizing a second level ofresolution to produce a first remaining portion of the video frames forthe virtual reality environment with regards to the first set of objectrepresentations. The second level of resolution is a lower videoresolution level than the first level of resolution. The lowerresolution level is suitable for less important aspects of the virtualreality environment with regards to information retention. Utilizing alower resolution can help to save processing power in both the creationof the video frames and the subsequent displaying of the video frames.The remaining portion of the video frames with regards to the first setof object representations is associated with further aspects that arenot covered by the priority asset video frames. For, the experiencecreation module 30 renders the other representation of the first set ofobject representations utilizing the second level resolution to producethe first remaining portion of video frames associated with the sparkplug, the valves opening and closing, and the piston moving through thecylinder during the preparation to fire of the intake and compressionstrokes.

The fifth step of the example method of operation further includes theexperience creation module 30 rendering another representation of thesecond set of object representations utilizing the second level ofresolution to produce a second remaining portion of the video frames forthe virtual reality environment with regards to the second set of objectrepresentations. For example, the experience creation module 30 rendersthe other representation of the second set of object representationsutilizing the second-level resolution to produce the second remainingportion of video frames with regards to the spark plug firing, thevalves opening and closing, and the piston moving through the cylinderduring the firing and exhaust strokes.

FIG. 10C further illustrates the example method of operation of thegenerating the virtual reality environment, where, having produced videoframes for the virtual reality environment, a sixth step includes theexperience creation module 30 linking the common portion, the firstremaining portion, and the second remaining portion of the video framesto produce the virtual reality environment. For example, the experiencecreation module 30 creates a set of lesson assets 711 from the videoframes and the preliminary set of lesson assets. For instance, theexperience creation module 30 combines the preliminary set of lessonassets with the video frames of the common portion, the first remainingportion, and the second remaining portion to produce the set of lessonasset 711 as the virtual reality environment. Alternatively, or inaddition to, the experience creation module 30 combines the preliminaryset of lesson assets with the video frames to produce a lesson package206 for storage in the learning assets database 34.

Having generated the set of lesson assets 711, a seventh step of theexample method of operation to generate the virtual reality environmentincludes the experience creation module 30 outputting the set of lessonasset 711 as a lesson package 206 for interactive consumption. Forexample, the experience creation module 30 utilizes the high-resolutionvideo frames and the low resolution video frames for the objects togenerate the lesson asset video frames 713. Having generated the lessonasset video frames 713, the experience creation module 30 outputslearner output information 172 to the human interface module 18, wherethe learner output information 172 includes the lesson asset videoframes 713. The human interface module 18 outputs human output 162 to astudent to interactively consume the lesson package.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 11A, 11B, and 11C are schematic block diagrams of an embodiment ofa computing system illustrating an example of generating multipleresolutions of a virtual reality environment. The computing systemincludes the human interface module 18 of FIG. 1 , the experiencecreation module 30 of FIG. 1 , the experience execution module 32 ofFIG. 1 (e.g., of FIG. 11C), the learning assets database 34 FIG. 1 , andthe environment model database 16 of FIG. 1 .

FIG. 11A illustrates an example method of operation of the generating ofthe multiple resolutions of the virtual reality environment where afirst step includes the experience creation module 30 generating thevirtual reality environment utilizing a group of object representationsin accordance with interaction information for at least some of theobject representations of the group of object representations. At leastsome of the object representations are associated with correspondingthree dimensional (3-D) physical objects. The interaction informationincludes 3-D models and position information for the at least some ofthe object representations of the group of object representations. Afirst set of object representations of the group of objectrepresentations is associated with a first piece of informationregarding a topic. A second set of object representations of the groupof object representations is associated with a second piece ofinformation regarding the topic.

A first step of the example method of operation includes the experiencecreation module 30 obtaining the first and second pieces of informationfor the topic. The obtaining includes creating first and second sets ofknowledge bullet-points of a plurality of learning objects for thetopic. A first learning object of the plurality of learning objectsincludes a first set of knowledge bullet-points for a first piece ofinformation regarding the topic. A second learning object of theplurality of learning objects includes a second set of knowledgebullet-points for a second piece of information regarding the topic. Forexample, the experience creation module 30 receives instructor inputinformation 166, through an appropriate user interface, from the humaninterface module 18 in response to human input 164 from an instructorwhen the topic is how a four stroke internal combustion engine operates.The experience creation module 30 interprets instructor inputinformation 166 to generate the first set of knowledge bullet-points702-1 to include “intake stroke: intake valve opens, air/fuel mixturepulled into cylinder by piston; compression stroke: intake valve closes,piston compresses air/fuel mixture in cylinder.”

The experience creation module 30 interprets further instructor inputinformation 166 to generate the second set of knowledge bullet-points702-2. For example, the experience creation module 30 generates thesecond set of knowledge bullet-points to include “power stroke:sparkplug ignites air/fuel mixture pushing piston; exhaust stroke:exhaust valve opens and piston pushes exhaust out of cylinder, exhaustvalve closes”.

A second step of the example method of operation includes the experiencecreation module 30 obtaining object representations for the topic bydetermining a preliminary set of lesson assets 705 based on the firstand second learning objects so far and modeled asset information 200.The preliminary set of lesson assets includes a first descriptive assetassociated with the first set of knowledge bullet-points and a seconddescriptive asset associated with the second set of knowledgebullet-points. The first learning object further includes a firstdescriptive asset regarding the first piece of information based on thefirst set of knowledge bullet-points and illustrative assets 704. Thesecond learning object further includes a second descriptive assetregarding the second piece of information based on the second set ofknowledge bullet-points and the common illustrative assets.

For example, the experience creation module 30 determines thepreliminary set of lesson asset 705 to include instructions for each ofthe four strokes (e.g., part of the bullet-points). The preliminary setof lesson asset 705 further includes engine depiction assets for each ofthe four strokes that utilize the common illustrative assets 704 andutilize models of the internal combustion engine from the modeled assetinformation 200.

Alternatively, or in addition to, the experience creation module 30produces the object representations via a series of sub-steps. A firstsub-step includes the experience creation module outputting arepresentation of a set of common illustrative assets as instructoroutput information. For example, descriptions of the cylinder, thepiston, the spark plug, the intake valve, and the exhaust valve.

A second sub-step includes receiving instructor input information 166 inresponse to the instructor output information. For example, theinstructor input information 166 includes guidance with regards to howthe common illustrative assets operate together to produce the fourstrokes of the engine.

A third sub-step includes interpreting the instructor input information166 to produce at least some of the group of object representations asthe preliminary set of lesson assets 705. For example, the experiencecreation module 30 generates the preliminary set of lesson assets 705 toinclude instruction information for each of the four strokes utilizingthe common illustrative assets.

Further alternatively, or further in addition to, the experiencecreation module 30 produces the group of object representations via aseries of operations. A first operation includes the experience creationmodule 30 interpreting the first set of knowledge bullet points of thetopic to produce the first piece of information regarding the topic. Forexample, the experience creation module 30 interprets the intake andcompression strokes bullet points to produce the first piece ofinformation with regards to preparing the cylinder for firing.

A second operation includes the experience creation module 30 obtainingthe first set of object representations based on the first piece ofinformation regarding the topic. For example, the experience creationmodule 30 identifies the first set of object representations frommodeled asset information 200 from the environment model database 16based on the first piece of information for preparing the cylinder forfiring.

A third operation includes the experience creation module 30interpreting the second set of knowledge bullet points of the topic toproduce the second piece of information regarding the topic. Forexample, the experience creation module 30 interprets the power andexhaust strokes bullet points to produce the second piece of informationwith regards to firing the cylinder.

A fourth operation includes the experience creation module 30 obtainingthe second set of object representations based on the second piece ofinformation regarding the topic. For example, the experience creationmodule 30 identifies the second set of object representations from themodeled asset information 200 based on the second piece of informationfor firing the cylinder.

Having obtained the object representations for the topic, a third stepof the example method of operation includes the experience creationmodule 30 identifying a set of common illustrative assets asillustrative assets 704 based on the first and second set of objectrepresentations. The set of common illustrative assets belongs to thefirst and second sets of object representations and depict one or moreaspects regarding the topic pertaining to the first and second pieces ofinformation.

The identifying the set of common illustrative assets includes a varietyof approaches. A first approach includes interpreting instructor inputinformation to identify the common illustrative assets. For example, theexperience creation module 30 interprets instructor input information166 to extract the common illustrative assets.

A second approach includes identifying a common object representation ofthe first and second sets of object representations as the set of commonillustrative assets. For example, the experience creation module 30determines that the piston asset is common to both the first and secondsets of object representations. As another example, the experiencecreation module 30 interprets the first and second set of knowledgebullet-points to identify common objects to produce the illustrativeasset 704. For instance, the experience creation module 30 generates theillustrative asset 704 to include cylinder, piston, sparkplug, intakevalve, exhaust valve.

FIG. 11B further illustrates the example method of operation of thegenerating of the multiple resolutions of the virtual realityenvironment, where, having produce the set of common illustrativeassets, a fourth step includes the experience creation module 30producing the first level resolution video frames for the virtualreality environment. The producing of the first level of resolution ofthe virtual reality environment includes a series of operations. A firstoperation includes rendering the set of common illustrative assetsutilizing a first level of resolution to produce a set of commonillustrative assets video frames. For example, the experience creationmodule 30 renders, utilizing the first level of resolution, objectrepresentations for the cylinder, the piston, the valves, and the sparkplug to produce a preliminary set of asset video frames 709.

A second operation includes selecting a subset of the set of commonillustrative assets video frames to produce a common portion of videoframes for the virtual reality environment with regards to the first andsecond sets of object representations to reduce duplicative rendering.The selecting the subset of the set of common illustrative assets videoframes to produce the common portion of video frames for the virtualreality environment with regards to the first and second sets of objectrepresentations includes a series of sub-steps. A first sub-stepincludes identifying a first priority asset video frame of the set ofcommon illustrative assets video frames that represents a first aspectof the first set of object representations. For instance, a frame of thepiston. A second sub-step includes identifying a second priority assetvideo frame of the set of priority asset video frames that represents asecond aspect of the second set of object representations. For instance,another frame of the piston. A third sub-step includes establishing thecommon portion of video frames to include the first priority asset videoframe when more than a minimum threshold number of pixels of the firstand second priority asset video frames are the same. For instance, thefirst priority asset video frame is established when it is substantiallythe same as the second priority asset video frame.

A third operation of the producing of the first level of resolution ofthe virtual reality environment includes rendering anotherrepresentation of the first set of object representations utilizing thefirst level of resolution to produce a first remaining portion of thevideo frames for the virtual reality environment with regards to thefirst set of object representations. For example, the experiencecreation module 30 renders another portion of the first set of objectrepresentations that was not included in the set of common illustrativeassets.

A fourth operation includes rendering another representation of thesecond set of object representations utilizing the first level ofresolution to produce a second remaining portion of the video frames forthe virtual reality environment with regards to the second set of objectrepresentations. For example, the experience creation module 30 rendersanother portion of the second set of object representations that was notincluded in the set of common illustrative assets.

Having produced the first level resolution video frames, a fifth step ofthe example method of operation includes linking the common portion, thefirst remaining portion, and the second remaining portion of the videoframes to produce a first level of resolution of the virtual realityenvironment. For example, the experience creation module aggregates inorder the common portion of video frames, the first remaining portion ofvideo frames, and the second remaining portion of the video frames toproduce the first level of resolution of the virtual realityenvironment.

As another example, the experience creation module 30 creates a set oflesson assets 711 from the video frames and the preliminary set oflesson assets. For instance, the experience creation module 30 combinesthe preliminary set of lesson assets with the video frames of the commonportion, the first remaining portion, and the second remaining portionto produce the set of lesson asset 711 as the virtual realityenvironment. Alternatively, or in addition to, the experience creationmodule 30 combines the preliminary set of lesson assets with the videoframes to produce a lesson package 206 for storage in the learningassets database 34.

Having produced the first level of resolution of the virtual realityenvironment, the experience creation module 30 outputs a representationof the first level of resolution of the virtual reality environment toat least one of a learning asset database and a human interface module.For example, the experience creation module 30 outputs the lessonpackage 206 to the learning assets database 34 for storage where thelesson package 206 includes the first level resolution of the virtualreality environment. As another example, the experience creation module30 outputs learner output information 170 to via the human interfacemodule 18 as human output 162 for interactive consumption, where thelearner output information 172 includes the first level resolution ofthe virtual reality environment.

FIG. 11C further illustrates the example method of operation of thegenerating of the multiple resolutions of the virtual realityenvironment, where, having produced, linked, and output the firstresolution video frames, a sixth step of the example method of operationincludes generating a second level of resolution of the virtual realityenvironment based on a priority asset 707 of the set of commonillustrative assets. The second level of resolution is a higher videoresolution level than the first level of resolution.

The sixth step includes determining the priority asset of the set ofcommon illustrative assets. The determining the priority asset includesa series of sub-steps. A first sub-step includes determining a firstimportance status level of a first common illustrative asset of the setof common illustrative assets for example, the experience creationmodule 30 interprets modeled asset information 200 with regards to thepiston object to reveal the first importance status level of the pistonobject.

A second sub-step includes comparing the first importance status levelto the importance status threshold level with regards to the topic. Forexample, the experience creation module 30 interprets the modeled assetinformation 200 with regards to the topic to reveal the importancestatus threshold level with regards to the engine operation topic. Theexperience creation module 30 compares the importance status level ofthe piston to the importance status level threshold with regards to theengine topic.

A third sub-step includes establishing the first common illustrativeasset as the priority asset when the first importance status level isgreater than the importance status threshold level with regards to thetopic. For example, the experience creation module 30 establishes thepiston asset as the priority asset when the importance status level ofthe piston is greater than the importance status threshold level.

The sixth step of the example method of operation further includes theexperience creation module 30 generating the second level of resolutionof the virtual reality environment based on the priority asset of theset of common illustrative assets by a series of sub-steps. A firstsub-step includes determining the priority asset 707 of the set ofcommon illustrative assets as discussed above. The priority asset isassociated with the importance status level that is greater than theimportance status threshold level with regards to the topic.

A second sub-step includes rendering the priority asset utilizing thesecond level of resolution to produce a set of priority asset videoframes 715. For example, the experience creation module 30 renders theobject representation of the piston to produce the set of priority assetvideo frames that realize the higher second level of resolution.

A third sub-step includes selecting a subset of the set of priorityasset video frames to produce an updated common portion of video framesfor the virtual reality environment with regards to the first and secondsets of object representations. For example, the experience executionmodule 32 selects video frames of the piston that are associated withimproved learning when the higher resolution is utilized. For instance,when the fuel explodes in the cylinder above the piston during the powerstroke to push the piston into the cylinder is an important aspect ofthe operation of the piston and the enhanced high-resolution of thesecond resolution level can help facilitate improved learning.

The sixth step of the example method of operation further includes theexperience execution module 32 linking the updated common portion, thefirst remaining portion, and the second remaining portion of the videoframes to produce the second level of resolution of the virtual realityenvironment. For example, the experience execution module 32 aggregatesall the video frames to produce lesson asset video frames 713 as part ofthe lesson package 206.

Having produced the second level of resolution of the virtual realityenvironment, the experience execution module 32 outputs a representationof the second level of resolution of the virtual reality environment toat least one of the learning asset database and the human interfacemodule. For example, the experience execution module 32 stores thelesson package 206 and the learning assets database 34, where the lessonpackage 206 includes the second level of resolution of the virtualreality environment. As another example, the experience execution module32 outputs learner output information 170 to via the human interfacemodule 18 as human output 162 for interactive consumption, where thelearner output information 172 includes the second level of resolutionof the virtual reality environment.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 12A, 12B, and 12C are schematic block diagrams of an embodiment ofa computing system illustrating an example of updating a virtual realityenvironment. The computing system includes the human interface module 18of FIG. 1 , the experience creation module 30 of FIG. 1 , the learningassets database 34 FIG. 1 , and the environment model database 16 ofFIG. 1 .

FIG. 12A illustrates an example method of operation of the updating avirtual reality environment includes the experience creation module 30generating the virtual reality environment utilizing a group of objectrepresentations in accordance with interaction information for at leastsome of the object representations of the group of objectrepresentations. At least some of the object representations areassociated with corresponding three dimensional (3-D) physical objects.The interaction information includes 3-D models and position informationfor the at least some of the object representations of the group ofobject representations. A first set of object representations of thegroup of object representations is associated with a first piece ofinformation regarding a topic. A second set of object representations ofthe group of object representations is associated with a second piece ofinformation regarding the topic.

A first step of the example method of operation includes the experiencecreation module 30 obtaining the first and second pieces of informationfor the topic. The obtaining includes creating first and second sets ofknowledge bullet-points of a plurality of learning objects for thetopic. A first learning object of the plurality of learning objectsincludes a first set of knowledge bullet-points for a first piece ofinformation regarding the topic. A second learning object of theplurality of learning objects includes a second set of knowledgebullet-points for a second piece of information regarding the topic. Forexample, the experience creation module 30 receives instructor inputinformation 166 as previously discussed with reference to FIG. 11A,through an appropriate user interface, from the human interface module18 in response to human input 164 from an instructor when the topic ishow a four stroke internal combustion engine operates. The experiencecreation module 30 interprets instructor input information 166 togenerate the first set of knowledge bullet-points 702-1 to include“intake stroke: intake valve opens, air/fuel mixture pulled intocylinder by piston; compression stroke: intake valve closes, pistoncompresses air/fuel mixture in cylinder.”

The experience creation module 30 interprets further instructor inputinformation 166 to generate the second set of knowledge bullet-points702-2. For example, the experience creation module 30 generates thesecond set of knowledge bullet-points to include “power stroke:sparkplug ignites air/fuel mixture pushing piston; exhaust stroke:exhaust valve opens and piston pushes exhaust out of cylinder, exhaustvalve closes”.

A second step of the example method of operation includes the experiencecreation module 30 obtaining object representations for the topic bydetermining a set of lesson assets 711 based on the first and secondlearning objects. The set of lesson assets includes a first descriptiveasset associated with the first set of knowledge bullet-points and asecond descriptive asset associated with the second set of knowledgebullet-points. The first learning object further includes a firstdescriptive asset regarding the first piece of information based on thefirst set of knowledge bullet-points. The second learning object furtherincludes a second descriptive asset regarding the second piece ofinformation based on the second set of knowledge bullet-points.

For example, the experience creation module 30 determines the set oflesson asset 711 to include instructions for each of the four strokes(e.g., part of the bullet-points). The set of lesson asset 711 furtherincludes engine depiction assets for each of the four strokes and thatutilize models of the internal combustion engine.

Alternatively, or in addition to, the experience creation module 30produces the group of object representations via a series of operations.A first operation includes the experience creation module 30interpreting the first set of knowledge bullet points of the topic toproduce the first piece of information regarding the topic. For example,the experience creation module 30 interprets the intake and compressionstrokes bullet points to produce the first piece of information withregards to preparing the cylinder for firing.

A second operation includes the experience creation module 30 obtainingthe first set of object representations based on the first piece ofinformation regarding the topic. For example, the experience creationmodule 30 identifies the first set of object representations frommodeled asset information from the environment model database 16 basedon the first piece of information for preparing the cylinder for firing.

A third operation includes the experience creation module 30interpreting the second set of knowledge bullet points of the topic toproduce the second piece of information regarding the topic. Forexample, the experience creation module 30 interprets the power andexhaust strokes bullet points to produce the second piece of informationwith regards to firing the cylinder.

A fourth operation includes the experience creation module 30 obtainingthe second set of object representations based on the second piece ofinformation regarding the topic. For example, the experience creationmodule 30 identifies the second set of object representations from themodeled asset information based on the second piece of information forfiring the cylinder.

Having obtained the object representations for the topic, a third stepof the example method of operation includes obtaining a first conveyanceeffectiveness level for a first portrayal of the first set of objectrepresentations within the virtual reality environment with regards tothe first piece of information. The obtaining the first conveyanceeffectiveness level for the first portrayal of the first set of objectrepresentations within the virtual reality environment with regards tothe first piece of information includes a series of sub-steps.

A first sub-step includes interpreting learner input information withregards to perceptions of effectiveness for the first portrayal of thefirst set of object representations within the virtual realityenvironment to produce student population feedback. For example, aneffectiveness evaluation module 728 of the experience creation module 30interprets learner input information 174 from the human interface module18 to produce student population feedback. The human interface module 18receives human input 164 that includes the student population feedback.The feedback includes student perceptions of effectiveness of the lessonpackage (e.g., overall, for portions).

Having produced the student population feedback, the effectivenessevaluation module 728 generates effectiveness information 730-1 through730-4 for each of the four strokes of the four stroke engine lessonpackage based on the student population feedback. For example, theeffectiveness evaluation module 728 produces the effectivenessinformation 730-1 for the intake stroke, where most students indicatedthat the instruction information and representative video frameseffectively conveyed the operation of the engine during the intakestroke. As another example, the effectiveness evaluation module 728produces the effectiveness information 730-3 for the power stroke, wheremost students indicated that the instruction information andrepresentative video frames did not effectively convey the operation ofthe engine during the power stroke.

A second sub-step of obtaining the first conveyance effectiveness levelincludes interpreting environment sensor information with regards tofurther perceptions of the effectiveness for the first portrayal of thefirst set of object representations within the virtual realityenvironment to produce general population feedback. For example, theeffectiveness evaluation module 728 receives environment sensorinformation 38 from the environment sensor module 14, where theenvironment sensor information 38 includes general population feedbackwith regards to effectiveness. In a similar fashion, the effectivenessevaluation module 728 evaluates the environment sensor information 38 toproduce the effectiveness information 730-1 through 730-4.

A third sub-step of obtaining the first conveyance effectiveness levelincludes evaluating at least one of the student population feedback andthe general population feedback to produce the first conveyanceeffectiveness level for the first portrayal of the first set of objectrepresentations. For example, the effectiveness evaluation module 728utilizes the student population feedback when available. As anotherexample, the effectiveness evaluation module 728 utilizes the generalpopulation feedback when it’s available. As yet another example, theeffectiveness evaluation module 728 utilizes both the student populationfeedback and the general population feedback when they are bothavailable.

FIG. 12B further illustrates the example method of operation for theupdating of the virtual reality environment where, when the firstconveyance effectiveness level is less than a minimum conveyanceeffectiveness threshold level, a fourth step includes the experiencecreation module 30 determining an updated first set of objectrepresentations based on the first set of object representations and thefirst conveyance effectiveness level. The determining the updated firstset of object representations based on the first set of objectrepresentations and the first conveyance effectiveness level includes aseries of sub-steps. A first sub-step includes identifying a deficiencyassociated with the first conveyance effectiveness level. For example,an effectiveness enhancement module 732 of the experience creationmodule 30 identifies the examples from the feedback, as recoveredhistory associated with lesson package 206 from the learning assetsdatabase 34, such when the students indicated that the instructioninformation and representative video frames did not effectively conveythe operation of the engine during the power stroke.

A second sub-step includes modifying the first set of objectrepresentations to address the deficiency to produce the updated firstset of object representations. For example, the effectivenessenhancement module 732 utilizes modeled asset information 200 from theenvironment model database 16 to produce the updated first set of objectto use second level frames for the piston associated with the secondthrough fourth strokes based on the effectiveness information 730-1through 730-4. For instance, the effectiveness enhancement module 732produces no changes for the first stroke to produce enhancementinformation 734-1 and replaces object representations for video framesfor the piston in the second through fourth strokes with the secondlevel frames of the piston to produce enhancement information 734-2through 734-4 (e.g., more detailed pistons and an explosionrepresentation for the power stroke).

Having produced the updated first set of object representations, a fifthstep of the example method of operation includes rendering arepresentation of the updated first set of object representations toproduce a first portion of video frames for a second portrayal of thefirst set of object representations for the virtual reality environment.Having produced the first portion of video frames for the secondportrayal of the first set of object representations, the effectivenessenhancement module 732 renders a representation of the second set ofobject representations to produce the second portion of video frames forthe second portrayal of the second set of object representations for thevirtual reality environment. For example, the effectiveness enhancementmodule 732 renders the updated first set of object representations toproduce the first portion of video frames for the second portrayal ofthe first set of object representations to include the enhancementinformation 734-1 through 734-2, that includes enhance piston videoframes, and renders the representation of the second set of objectrepresentations to produce the second portion of video frames to includeenhancement information 734-3 through 734-4, that includes a video frameof the explosion during the power stroke.

FIG. 12C further illustrates the example method of operation where asixth step includes the experience creation module 30 linking the firstportion of the video frames with a second portion of video frames for asecond portrayal of the second set of object representations for thevirtual reality environment to produce the virtual reality environment.For example, the experience creation module 30 uses the video framesfrom the enhanced information 734-1 through 734-4 to replacecorresponding frames of the first portrayal to produce lesson assetvideo frames 713 as the second portrayal.

Having produced the lesson asset video frames 713, the experiencecreation module 30 integrates the lesson asset video frames 713 with thelesson package 206 to produce the updated lesson package 206 for storagein the learning assets database 34. Alternatively, the experiencecreation module 30 outputs a representation of the virtual realityenvironment to at least one of the learning assets database 34 and thehuman interface module 18 associated with another student to experienceinteractive consumption with an improved conveyance effectiveness level.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 13A and 13B are schematic block diagrams of an embodiment of acomputing system illustrating an example of accessing a virtual realityenvironment. The computing system includes the experience executionmodule 32 of FIG. 1 , the learning assets database 34 FIG. 1 , and thehuman interface module 18 of FIG. 1 .

FIG. 13A illustrates an example method of operation of the accessing thevirtual reality environment that includes generating the virtual realityenvironment utilizing a group of object representations in accordancewith interaction information for at least some of the objectrepresentations of the group of object representations. At least some ofthe object representations are associated with corresponding threedimensional (3-D) physical objects. The interaction information includes3-D models and position information for the at least some of the objectrepresentations of the group of object representations. A first set ofobject representations of the group of object representations isassociated with a first piece of information regarding a topic. A secondset of object representations of the group of object representations isassociated with a second piece of information regarding the topic.

The generating the virtual reality environment includes determining thegroup of object representations utilizing a series of sub-steps. A firstsub-step includes the experience execution module 32 interpreting afirst set of knowledge bullet points of the topic to produce the firstpiece of information regarding the topic. For example, the experienceexecution module 32 interprets a lesson package 206 recovered from thelearning assets database with regards to a lesson timeline 740 toidentify the first set of knowledge bullet points that are associatedwith a bulldozer.

A second sub-step includes the experience execution module 32 obtainingthe first set of object representations based on the first piece ofinformation regarding the topic. For example, the experience executionmodule 32 further interprets the lesson package 206 to extract objectrepresentations for various aspects of the bulldozer.

A third sub-step includes the experience execution module 32interpreting a second set of knowledge bullet points of the topic toproduce the second piece of information regarding the topic. Forexample, the experience execution module 32 further interprets thelesson package 206 to identify the second set of knowledge bullet pointsthat are associated with a truck.

A fourth sub-step includes the experience execution module 32 obtainingthe second set of object representations based on the second piece ofinformation regarding the topic. For example, the experience executionmodule 32 further interprets the lesson package 206 to extract objectrepresentations for various aspects of the truck.

In an embodiment, the interpreting of the lesson package 206 furtherincludes identifying priority assets for the lesson timeline 740. Forexample, the experience execution module 32 interprets the lessonpackage 206 to identify priority assets based on one or more of apredetermination, a user input, a major asset identification algorithmoutput, and an impact level metric. For example, the experienceexecution module 32 identifies the bulldozer, the truck, and a bridge asthe set of priority assets of the lesson package when each areassociated with indicators of high-priority.

Having identified the priority assets, the experience execution module32 identifies lesson time frames for the set of priority assets. Forexample, the experience execution module 32 interprets video frames ofthe lesson package 206 to identify time codes associated with a portionof the lesson package associated with a first representation of each ofthe priority assets. For example, the experience execution module 34identifies a timecode of 0:10.0 associated with a first portrayal of thebulldozer, a timecode of 01:25.4 associated with a first portrayal ofthe truck, and a timecode of 3:03.1 associated with the first portrayalof the bridge.

Having determined the lesson time frames, a second step of the examplemethod of operation to access the virtual reality environment includesrendering a representation of the first set of object representationsand the second set of object representations to produce first portrayal3-D video frames of the first piece of information for the virtualreality environment. The first portrayal 3-D video frames include asecond cue associated with the second set of object representations.

The rendering the representation of the first set of objectrepresentations and the second set of object representations to producethe first portrayal 3-D video frames of the first piece of informationfor the virtual reality environment includes a series of sub-steps. Afirst sub-step includes the experience execution module 32 rendering therepresentation of the first set of object representations to producefirst object 3-D video frames. For example, the experience executionmodule 32 renders the representation of the bulldozer to produce thefirst object 3-D video frames.

A second sub-step includes the experience execution module 32 renderingthe representation of the second set of object representations toproduce second object 3-D video frames. For example, the experienceexecution module 32 renders the representation of the truck to producethe second object 3-D video frames.

A third sub-step includes the experience execution module 32 overlayinga representation of a portion of the second object 3-D video frames overthe first object 3-D video frames to produce the first portrayal 3-Dvideo frames of the first piece of information. The overlaying therepresentation of the portion of the second object 3-D video frames overthe first object 3-D video frames to produce the first portrayal 3-Dvideo frames of the first piece of information includes a series offurther steps. A first further step includes the experience executionmodule 32 generating the representation of the portion of the secondobject 3-D video frames to produce the second cue associated with thesecond set of object representations. The second cue includes one ormore of an icon, a priority asset icon, a scaled image capture, ahighlighted object, a flashing object, a color shifted object, text, atime reference, a video loop, an outline, a shadow image, a stickfigure, an avatar, and a video scroll bar. For example, the expenseexecution module 32 produces a small icon of the truck (e.g., ascompared to size of images of the bulldozer) as the second cue.

A second further step includes the experience execution module 32integrating the second cue with the first object 3-D video frames toproduce the first portrayal 3-D video frames of the first piece ofinformation. For example, the experience execution module 32 generatesthe first portrayal 3D video frames to include the bulldozer and thesmall icon of the truck to serve as the second cue to enable subsequentselection of the virtual reality environment associated with the truck.

Having generated the first portrayal 3-D video frames, the experienceexecution module 32 updates the lesson package 206 to include at leastone representation of the first portrayal 3-D video frames to producelesson asset video frames 713. For example, the experience executionmodule 32 generates the lesson asset video frames 713 to include smallicons of the truck and bridge to be portrayed during a portion of thelesson package associated with the bulldozer. As another example, theexperience execution module 32 generates the video scrollbar at toinclude time relative icons of the bulldozer in the bridge for anotherportion of the lesson package associated with the truck. As yet anotherexample, the experience execution module 32 generates the lesson assetvideo frames 713 to include a blinking icon of the truck and a shadowedicon of the bulldozer for yet another portion of the lesson packageassociated with the bridge.

Having updated the lesson package 206, the experience execution moduleoutputs learner output information 172 to the human interface module 18where the learner output information 172 includes the lesson asset videoframes 713. The human interface module 18 outputs human output 162 to astudent, where the human output 162 includes the lesson asset videoframes 713 such that the student can select one of the priority assetsto facilitate immediately moving to the portion of the lesson packageassociated with that selected priority asset.

Having generated the first portrayal 3-D video frames, a third step ofthe example method operation to access the virtual reality environmentincludes determining whether the second piece of information has beenselected based on the second cue. The determining whether the secondpiece of information has been selected based on the second cue includesseries of sub-steps. A first sub-step includes the experience executionmodule 32 interpreting learner input information 174 with regards toportrayal of the second cue within the virtual reality environment toproduce student feedback. For example, the human interface module 18receives human input 164 from the student to select the second cue andthe human interface module 18 outputs the learner input information 174to the experience execution module 32 to indicate the selection of thesecond cue.

A second sub-step includes indicating that the second piece ofinformation has been selected when the student feedback includes anactivation of the second cue. For example, the experience executionmodule 32 indicates that the second piece of information has beenselected when the selection of the second cue has been activated by thestudent.

FIG. 13B further illustrates the example method of operation for theaccessing the virtual reality environment where, when the second pieceof information has been selected, a fourth step includes renderinganother representation of the second set of object representations andthe first set of object representations to produce second portrayal 3-Dvideo frames of the second piece of information for the virtual realityenvironment. The second portrayal 3-D video frames include a first cueassociated with the first set of object representations. For example,the experience execution module 32 renders the object representationsassociated with the truck for the second piece of information along withthe object representations associated with the bulldozer to produce afirst cue such that the student can subsequently redirect the lessontimeline 740 and resulting lesson asset video frames 713 back to thefirst piece of information associated with the bulldozer.

Having produced lesson asset video frames 713, a fifth step of theexample method of operation includes outputting the second portrayal 3-Dvideo frames. For example, the outputting includes the experienceexecution module 32 outputting a representation of the virtual realityenvironment to at least one of the learning asset database 34 (e.g., asan updated lesson package 206) and the human interface module 18 asfurther learner output information 172 representing the lesson assetvideo frames 713.

In an embodiment, the selection of the priority asset and cues includesone or more of a user input selection, where a majority of students jumpto now, where this particular student should jump to that is best forthem based on one or more of their learning history and capabilitylevel. For example, while outputting lesson asset video frames 713 tothe student via the human interface module 18 (e.g., outputting thelearner output information 172 for representation by the human interfacemodule 18 as human output 162) the portion of the lesson packageassociated with the bulldozer, the experience execution module 32determines to jump to the portion associated with the truck based oninterpreting learner input information 174 from the human interfacemodule 18, where the student provided human input 164 to the humaninterface module 18 indicating that they need to learn more about thetruck.

Alternatively, or in addition to, the experience execution module 32changes the cue after jumping. For example, changing from small iconsalong the video scrollbar to flashing icons dropped onto a primaryportion of the rendering, or any other possible representation ofobjects of a topic based on a cue.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

FIGS. 14A, 14B, and 14C are schematic block diagrams of an embodiment ofa computing system illustrating another example of updating a lessonpackage. The computing system includes the experience execution module32 of FIG. 1 , the learning assets database 34 of FIG. 1 , and the humaninterface module 18 of FIG. 1 .

FIG. 14A illustrates an example method of operation of the updating ofthe lesson package that includes the experience execution module 32generating learner-specific assessment assets for the lesson package.For example, instance experience module 290 of the experience executionmodule 32 generates a representation of a first set of learner-specificassessment assets of a first learning object of a plurality of learningobjects 810 extracted from learning asset information 48 from the lessonpackage 206 recovered from the learning assets database 34. Inassessment asset is utilized to portray a portion of an assessment. Alearner-specific assessment asset conveys a portion of an assessmentrelated to a specific student.

The first learning object includes a first set of knowledgebullet-points for a first piece of information regarding the topic. Asecond learning object of the plurality of learning objects includes asecond set of knowledge bullet-points for a second piece of informationregarding the topic. The first learning object and the second learningobject further include an illustrative asset that depicts an aspectregarding the topic pertaining to the first and the second pieces ofinformation, wherein the first learning object further includes a firstdescriptive asset regarding the first piece of information based on thefirst set of knowledge bullet-points and the illustrative asset.

The second learning object further includes a second descriptive assetregarding the second piece of information based on the second set ofknowledge bullet-points and the illustrative asset.

The instance experience module 290 determines which learner-specificassessment assets to generate based on one or more of an identity of thestudent, a history of learning by the student, an estimated learningcapability level of the student, and an expected comprehension levelassociated with the lesson package. For example, the instance experiencemodule 290 selects the learner-specific assessment asset to includeasking the student “what is the scoop?” of the DEE 6 bulldozer of thelesson package when an expected responses within an expected range ofcorrectness for similar students.

Having selected the learner-specific assessment assets, a second step ofthe example method of operation to update the lesson package includesthe instance experience module 290 outputting the learner-specificassessment assets. For example, the instance experience module 290outputs assessment asset video frames 800 associated with thelearner-specific assessment assets to the human interface module aslearner output information 172. The human interface module 18 outputshuman output 162 2 the student to include the assessment asset videoframes 800 (e.g., a portrayal of the bulldozer asking what is thescoop?).

FIG. 14B further illustrates the example method of operation of theupdating of the lesson package where a third step includes theexperience execution module 32 obtaining an assessment response inresponse to the learner-specific assessment assets. For example, alearning assessment module 330 interprets learner input information 174from the human interface module 18 to extract an assessment response802, where the human interface module 18 receives human input 164 fromthe student that includes a response.

Having obtained the assessment response, a fourth step of the examplemethod of operation includes the learning assessment module 330determining an undesired performance aspect of the assessment response.For example, the learning assessment module 330 interprets theassessment response 802 to identify a first answer that includes a doorof the bulldozer as the undesired performance aspect (e.g., the door notthe scoop). As another example, the learning assessment module 330interprets the assessment response 802 to identify a second answer thatincludes the actual scoop of the bulldozer as a desired performanceaspect (e.g., the scoop as the correct answer).

FIG. 14C further illustrates the example method of operation where afifth step includes the experience execution module 32 updating thelearning objects to update the lesson package. The updating includesidentifying a modification for the learning object to be updated basedon the undesired performance aspect. For instance, when the studentidentified the door as the scoop incorrectly, the learning assessmentmodule 330 determines the modification to include modifying a lessonpackage to further highlight the scoop of the bulldozer to make clearwhat portion of the bulldozer is the scoop and not the door.

Having identified the modification, the learning assessment module 330re-renders a portion of the learning objects 810 to include themodification as updated learning objects 812. The learning assessmentmodule 330 stores the updated learning objects 812 as the lesson package206 in the learning assets database 34 to complete the updating of thelesson package.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element, a sixth memoryelement, etc.) that stores operational instructions can, when executedby one or more processing modules of the one or more computing devicesof the computing system 10, cause the one or more computing devices toperform any or all of the method steps described above.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, text, graphics, audio, etc. any of which may generally bereferred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. For some industries, anindustry-accepted tolerance is less than one percent and, for otherindustries, the industry-accepted tolerance is 10 percent or more. Otherexamples of industry-accepted tolerance range from less than one percentto fifty percent. Industry-accepted tolerances correspond to, but arenot limited to, component values, integrated circuit process variations,temperature variations, rise and fall times, thermal noise, dimensions,signaling errors, dropped packets, temperatures, pressures, materialcompositions, and/or performance metrics. Within an industry, tolerancevariances of accepted tolerances may be more or less than a percentagelevel (e.g., dimension tolerance of less than +/-1%). Some relativitybetween items may range from a difference of less than a percentagelevel to a few percent. Other relativity between items may range from adifference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operablycoupled to”, “coupled to”, and/or “coupling” includes direct couplingbetween items and/or indirect coupling between items via an interveningitem (e.g., an item includes, but is not limited to, a component, anelement, a circuit, and/or a module) where, for an example of indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operableto”, “coupled to”, or “operably coupled to” indicates that an itemincludes one or more of power connections, input(s), output(s), etc., toperform, when activated, one or more its corresponding functions and mayfurther include inferred coupling to one or more other items. As maystill further be used herein, the term “associated with”, includesdirect and/or indirect coupling of separate items and/or one item beingembedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may be used herein, one or more claims may include, in a specificform of this generic form, the phrase “at least one of a, b, and c” orof this generic form “at least one of a, b, or c”, with more or lesselements than “a”, “b”, and “c”. In either phrasing, the phrases are tobe interpreted identically. In particular, “at least one of a, b, and c”is equivalent to “at least one of a, b, or c” and shall mean a, b,and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and“b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, “processing circuitry”, and/or “processing unit”may be a single processing device or a plurality of processing devices.Such a processing device may be a microprocessor, microcontroller,digital signal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, processing circuitry, and/or processing unitmay be, or further include, memory and/or an integrated memory element,which may be a single memory device, a plurality of memory devices,and/or embedded circuitry of another processing module, module,processing circuit, processing circuitry, and/or processing unit. Such amemory device may be a read-only memory, random access memory, volatilememory, non-volatile memory, static memory, dynamic memory, flashmemory, cache memory, and/or any device that stores digital information.Note that if the processing module, module, processing circuit,processing circuitry, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,processing circuitry and/or processing unit implements one or more ofits functions via a state machine, analog circuitry, digital circuitry,and/or logic circuitry, the memory and/or memory element storing thecorresponding operational instructions may be embedded within, orexternal to, the circuitry comprising the state machine, analogcircuitry, digital circuitry, and/or logic circuitry. Still further notethat, the memory element may store, and the processing module, module,processing circuit, processing circuitry and/or processing unitexecutes, hard coded and/or operational instructions corresponding to atleast some of the steps and/or functions illustrated in one or more ofthe Figures. Such a memory device or memory element can be included inan article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with one or more other routines. In addition, a flow diagrammay include an “end” and/or “continue” indication. The “end” and/or“continue” indications reflect that the steps presented can end asdescribed and shown or optionally be incorporated in or otherwise usedin conjunction with one or more other routines. In this context, “start”indicates the beginning of the first step presented and may be precededby other activities not specifically shown. Further, the “continue”indication reflects that the steps presented may be performed multipletimes and/or may be succeeded by other activities not specificallyshown. Further, while a flow diagram indicates a particular ordering ofsteps, other orderings are likewise possible provided that theprinciples of causality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, a quantum register or otherquantum memory and/or any other device that stores data in anon-transitory manner. Furthermore, the memory device may be in a formof a solid-state memory, a hard drive memory or other disk storage,cloud memory, thumb drive, server memory, computing device memory,and/or other non-transitory medium for storing data. The storage of dataincludes temporary storage (i.e., data is lost when power is removedfrom the memory element) and/or persistent storage (i.e., data isretained when power is removed from the memory element). As used herein,a transitory medium shall mean one or more of: (a) a wired or wirelessmedium for the transportation of data as a signal from one computingdevice to another computing device for temporary storage or persistentstorage; (b) a wired or wireless medium for the transportation of dataas a signal within a computing device from one element of the computingdevice to another element of the computing device for temporary storageor persistent storage; (c) a wired or wireless medium for thetransportation of data as a signal from one computing device to anothercomputing device for processing the data by the other computing device;and (d) a wired or wireless medium for the transportation of data as asignal within a computing device from one element of the computingdevice to another element of the computing device for processing thedata by the other element of the computing device. As may be usedherein, a non-transitory computer readable memory is substantiallyequivalent to a computer readable memory. A non-transitory computerreadable memory can also be referred to as a non-transitory computerreadable storage medium.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples.

What is claimed is:
 1. A method for updating a virtual realityenvironment regarding a topic, the method comprises: generating, by acomputing entity, the virtual reality environment utilizing a group ofobject representations in accordance with interaction information for atleast some of the object representations of the group of objectrepresentations, wherein at least some of the object representations areassociated with corresponding three dimensional (3-D) physical objects,wherein the interaction information includes 3-D models and positioninformation for the at least some of the object representations of thegroup of object representations, wherein a first set of objectrepresentations of the group of object representations is associatedwith a first piece of information regarding the topic, wherein a secondset of object representations of the group of object representations isassociated with a second piece of information regarding the topic,wherein the generating the virtual reality environment includes:rendering, by the computing entity, a representation of the first set ofobject representations and the second set of object representations toproduce first portrayal 3-D video frames of the first piece ofinformation for the virtual reality environment, wherein the firstportrayal 3-D video frames include a second cue associated with thesecond set of object representations; determining, by the computingentity, whether the second piece of information has been selected basedon the second cue; and when the second piece of information has beenselected: rendering, by the computing entity, another representation ofthe second set of object representations and the first set of objectrepresentations to produce second portrayal 3-D video frames of thesecond piece of information for the virtual reality environment, whereinthe second portrayal 3-D video frames include a first cue associatedwith the first set of object representations.
 2. The method of claim 1further comprises: outputting, by the computing entity, a representationof the virtual reality environment to at least one of a learning assetdatabase and a human interface module.
 3. The method of claim 1 furthercomprises: determining, by the computing entity, the group of objectrepresentations by: interpreting a first set of knowledge bullet pointsof the topic to produce the first piece of information regarding thetopic, obtaining the first set of object representations based on thefirst piece of information regarding the topic, interpreting a secondset of knowledge bullet points of the topic to produce the second pieceof information regarding the topic, and obtaining the second set ofobject representations based on the second piece of informationregarding the topic.
 4. The method of claim 1, wherein the rendering therepresentation of the first set of object representations and the secondset of object representations to produce the first portrayal 3-D videoframes of the first piece of information for the virtual realityenvironment comprises: rendering the representation of the first set ofobject representations to produce first object 3-D video frames;rendering the representation of the second set of object representationsto produce second object 3-D video frames; and overlaying arepresentation of a portion of the second object 3-D video frames overthe first object 3-D video frames to produce the first portrayal 3-Dvideo frames of the first piece of information.
 5. The method of claim4, wherein the overlaying the representation of the portion of thesecond object 3-D video frames over the first object 3-D video frames toproduce the first portrayal 3-D video frames of the first piece ofinformation comprises: generating the representation of the portion ofthe second object 3-D video frames to produce the second cue associatedwith the second set of object representations, wherein the second cueincludes one or more of an icon, a priority asset icon, a scaled imagecapture, a highlighted object, a flashing object, a color shiftedobject, text, a time reference, a video loop, an outline, a shadowimage, a stick figure, an avatar, and a video scroll bar; andintegrating the second cue with the first object 3-D video frames toproduce the first portrayal 3-D video frames of the first piece ofinformation.
 6. The method of claim 1, wherein the determining whetherthe second piece of information has been selected based on the secondcue comprises: interpreting learner input information with regards toportrayal of the second cue within the virtual reality environment toproduce student feedback; and indicating that the second piece ofinformation has been selected when the student feedback includes anactivation of the second cue.
 7. A computing device comprises: aninterface; a local memory; and a processing module operably coupled tothe interface and the local memory, wherein the local memory storesoperational instructions that, when executed by the processing module,causes the computing device to: generate a virtual reality environmentutilizing a group of object representations in accordance withinteraction information for at least some of the object representationsof the group of object representations, wherein at least some of theobject representations are associated with corresponding threedimensional (3-D) physical objects, wherein the interaction informationincludes 3-D models and position information for the at least some ofthe object representations of the group of object representations,wherein a first set of object representations of the group of objectrepresentations is associated with a first piece of informationregarding a topic, wherein a second set of obj ect representations ofthe group of object representations is associated with a second piece ofinformation regarding the topic, wherein the processing module functionto generate the virtual reality environment by: rendering arepresentation of the first set of object representations and the secondset of object representations to produce first portrayal 3-D videoframes of the first piece of information for the virtual realityenvironment, wherein the first portrayal 3-D video frames include asecond cue associated with the second set of object representations;determining whether the second piece of information has been selectedbased on the second cue; and when the second piece of information hasbeen selected: rendering another representation of the second set ofobject representations and the first set of object representations toproduce second portrayal 3-D video frames of the second piece ofinformation for the virtual reality environment, wherein the secondportrayal 3-D video frames include a first cue associated with the firstset of object representations.
 8. The computing device of claim 7,wherein the processing module further functions to: output, via theinterface, a representation of the virtual reality environment to atleast one of a learning asset database and a human interface module. 9.The computing device of claim 7, wherein the processing module furtherfunctions to: determine the group of object representations by:interpreting a first set of knowledge bullet points of the topic toproduce the first piece of information regarding the topic, obtaining,via the interface, the first set of object representations based on thefirst piece of information regarding the topic, interpreting a secondset of knowledge bullet points of the topic to produce the second pieceof information regarding the topic, and obtaining, via the interface,the second set of object representations based on the second piece ofinformation regarding the topic.
 10. The computing device of claim 7,wherein the processing module functions to render the representation ofthe first set of object representations and the second set of objectrepresentations to produce the first portrayal 3-D video frames of thefirst piece of information for the virtual reality environment by:rendering the representation of the first set of object representationsto produce first object 3-D video frames; rendering the representationof the second set of object representations to produce second object 3-Dvideo frames; and overlaying a representation of a portion of the secondobject 3-D video frames over the first object 3-D video frames toproduce the first portrayal 3-D video frames of the first piece ofinformation.
 11. The computing device of claim 10, wherein theprocessing module functions to overlay the representation of the portionof the second object 3-D video frames over the first object 3-D videoframes to produce the first portrayal 3-D video frames of the firstpiece of information by: generating the representation of the portion ofthe second object 3-D video frames to produce the second cue associatedwith the second set of object representations, wherein the second cueincludes one or more of an icon, a priority asset icon, a scaled imagecapture, a highlighted object, a flashing object, a color shiftedobject, text, a time reference, a video loop, an outline, a shadowimage, a stick figure, an avatar, and a video scroll bar; andintegrating the second cue with the first object 3-D video frames toproduce the first portrayal 3-D video frames of the first piece ofinformation.
 12. The computing device of claim 7, wherein the processingmodule functions to determine whether the second piece of informationhas been selected based on the second cue by: interpreting learner inputinformation with regards to portrayal of the second cue within thevirtual reality environment to produce student feedback; and indicatingthat the second piece of information has been selected when the studentfeedback includes an activation of the second cue.
 13. A non-transitorycomputer readable memory comprises: a first memory element that storesoperational instructions that, when executed by a processing module,causes the processing module to: generate a virtual reality environmentutilizing a group of object representations in accordance withinteraction information for at least some of the object representationsof the group of object representations, wherein at least some of theobject representations are associated with corresponding threedimensional (3-D) physical objects, wherein the interaction informationincludes 3-D models and position information for the at least some ofthe object representations of the group of object representations,wherein a first set of object representations of the group of objectrepresentations is associated with a first piece of informationregarding a topic, wherein a second set of obj ect representations ofthe group of object representations is associated with a second piece ofinformation regarding the topic, wherein the processing module functionsto generate the virtual reality environment by: rendering arepresentation of the first set of object representations and the secondset of object representations to produce first portrayal 3-D videoframes of the first piece of information for the virtual realityenvironment, wherein the first portrayal 3-D video frames include asecond cue associated with the second set of object representations; anddetermining whether the second piece of information has been selectedbased on the second cue; and a second memory element that storesoperational instructions that, when executed by the processing module,causes the processing module to further generate the virtual realityenvironment by: when the second piece of information has been selected:rendering another representation of the second set of objectrepresentations and the first set of object representations to producesecond portrayal 3-D video frames of the second piece of information forthe virtual reality environment, wherein the second portrayal 3-D videoframes include a first cue associated with the first set of objectrepresentations.
 14. The non-transitory computer readable memory ofclaim 13 further comprises: a third memory element stores operationalinstructions that, when executed by the processing module, causes theprocessing module to: output a representation of the virtual realityenvironment to at least one of a learning asset database and a humaninterface module.
 15. The non-transitory computer readable memory ofclaim 13 further comprises: a fourth memory element stores operationalinstructions that, when executed by the processing module, causes theprocessing module to: determine the group of object representations by:interpreting a first set of knowledge bullet points of the topic toproduce the first piece of information regarding the topic, obtainingthe first set of object representations based on the first piece ofinformation regarding the topic, interpreting a second set of knowledgebullet points of the topic to produce the second piece of informationregarding the topic, and obtaining the second set of objectrepresentations based on the second piece of information regarding thetopic.
 16. The non-transitory computer readable memory of claim 13,wherein the processing module functions to execute the operationalinstructions stored by the first memory element to cause the processingmodule to render the representation of the first set of objectrepresentations and the second set of object representations to producethe first portrayal 3-D video frames of the first piece of informationfor the virtual reality environment by: rendering the representation ofthe first set of object representations to produce first object 3-Dvideo frames; rendering the representation of the second set of objectrepresentations to produce second object 3-D video frames; andoverlaying a representation of a portion of the second object 3-D videoframes over the first object 3-D video frames to produce the firstportrayal 3-D video frames of the first piece of information.
 17. Thenon-transitory computer readable memory of claim 16, wherein theprocessing module functions to execute the operational instructionsstored by the first memory element to cause the processing module tooverlay the representation of the portion of the second object 3-D videoframes over the first object 3-D video frames to produce the firstportrayal 3-D video frames of the first piece of information by:generating the representation of the portion of the second object 3-Dvideo frames to produce the second cue associated with the second set ofobject representations, wherein the second cue includes one or more ofan icon, a priority asset icon, a scaled image capture, a highlightedobject, a flashing object, a color shifted object, text, a timereference, a video loop, an outline, a shadow image, a stick figure, anavatar, and a video scroll bar; and integrating the second cue with thefirst object 3-D video frames to produce the first portrayal 3-D videoframes of the first piece of information.
 18. The non-transitorycomputer readable memory of claim 13, wherein the processing modulefunctions to execute the operational instructions stored by the firstmemory element to cause the processing module to determine whether thesecond piece of information has been selected based on the second cueby: interpreting learner input information with regards to portrayal ofthe second cue within the virtual reality environment to produce studentfeedback; and indicating that the second piece of information has beenselected when the student feedback includes an activation of the secondcue.